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FAN SZN with @IPXExchange As a creator, do I ever hold the raised money directly? Only the part that's unlocked. The rest stays in escrow until its stage is approved. Picture a concert tour: Ticket sales sit in a box-office account the tour manager can't touch Each stage's installment opens only after that stage is signed off The tour manager takes one installment at a time, never the whole box at once Here's how it works, per IPX: 1. Raised capital never sits in a creator's wallet. It's held in a non-custodial smart contract escrow 2. Before the raise, the creator submits a line-by-line budget. IPX says its AI agent, Alfred, sets it up in a contract tied to milestones, and that CertiK audited it 3. After the project graduates, its budget moves into a separate escrow. A stage unlocks once the creator submits proof and the platform approves it, and then it's available for the creator to transfer My read: as a creator, I'd hold an installment only after I prove its stage, and never the whole raise up front. Real count, from IPX's text: it names 3 example stages: pre-production, filming and post-production. Made-up scenario, not a real project: a film uses those 3 stages. Pre-production is approved, so 1 of 3 installments is available to transfer. Filming and post-production stay in escrow until they pass their checks. My take: the first thing I'd ask is how long an approval takes. If a stage sits locked while I wait, that could hold up the whole shoot. What comes out the other end, per IPX? • Each stage's installment, after its milestone is approved • A record of every request, approval and release on IPX and Base, so supporters can follow along • What a creator pays, if anything: I didn't find it listed, so I'd check the project terms The honest part: • IPX says unlocks happen automatically with no human gatekeepers, and also says the platform approves each milestone. I couldn't find how those fit together or how long an approval takes • The Defiant labels the announcement "Presented by IPX," and I haven't seen anyone outside IPX confirm how it runs • I haven't seen it run on a live project yet So before I launch, I'd find out who approves each milestone and how long an approval takes. I'll want both answers before I confirm. Why this matters for creators and fans: → Creators know the payout path before they launch → Fans can see which stages are open and follow each release → Both sides work from the same milestones Source: IPX's router announcement and its KPI-Gated Escrow announcement Which would you check first: the budget lines or who approves each milestone?

“Self-hosted Claude” deserves a second look before it goes into a procurement slide. The Nov 12 release lets you host the Managed Agents service: agents, execution, credentials and session history. Model requests still go to a chosen provider. It is a private beta. Draw two lines: what stays here, and what still leaves. “Private” is doing far too much work in that sentence.

《n8n 从零到能跑：六步路线》｜126 万播放的免费课，中文验收版 freeCodeCamp《n8n Tutorial – Zero to Hero Course》126 万播放，从节点概念一路到多 agent 编排。原视频： 我按「每步的验收标准」重排，方便边做边确认。 **1. 节点、架构、数据类型（04:50）** 验收：能说清一个 workflow 里数据是以什么结构在节点间传的。说不清，后面调试全靠猜。 **2. 第一个 agent workflow（12:48）** 一个聊天/邮件 agent 的最小回路。验收：输入一句话，能触发一次工具调用并拿到结果。 **3. 认证配置（45:06–56:15）** HTTP Request 节点的 API key、Google 系 OAuth。课程花了一小时讲这part，因为它最容易卡住。验收：凭证放在环境变量/凭据管理里，不硬编码在节点中。 **4. 接外部服务（1:41:48 起）** Slack、Google Sheets/Docs、任意 REST API。验收：故意让一个服务返回错误，workflow 不静默吞掉，而是走到错误分支。 **5. RAG 与多 agent 编排** 把检索放进流程、把任务分给不同节点。验收：能画出节点图，且图与实际执行顺序一致。 **6. 部署与可观测** 验收：失败时你能在执行日志里定位到具体节点，而不是只知道「没跑成功」。 **三条实操纪律** ① 每个 workflow 先做「失败路径」再做正常路径；② 凭证一律不进导出文件；③ 先在一个小任务上跑通全链路，再往上加分支。 **能带走的**：低代码不等于低工程。节点图画得清、错误分支写得好、凭证管得住，这三样决定它能不能从 demo 变成每天在跑的系统。

There's no pro plan on Orrery. There's your wallet. A node can list cheaper prices for $OREY holders. When your request comes in, it reads your balance on Solana and bills the lower price. Your router reads the same balance and won't sign for more. Nothing to sign up for. Nothing to renew. The discount is wherever your tokens are. One answer from today's rehearsal, start to signature.

We Built Shelter during the NEAR HACKATHON spend privately, anywhere onchain - Confidential balance via @near_intents + 1Click - New address per dapp (@WalletConnect ) - Blocks requests naming your wallets - NEP-413 signing via Meteor - NEAR AI in a TEE was great fun doubling down on this with @naz3eh 🙌 #NEARHackathon# @NEARProtocol

100k+ signatures in less than 24 hours! 💚 Over 5k current NVIDIANs, nearly 1k former NVIDIANs, and 54k+ shareholders. Thank you all! We hope the NVIDIA Board hears these voices and considers our requests. And to Jensen: Thank you. We stand with you. 💚 #standwithjensen# #bringjensenback#

This is f*cking insane. Anthropic CEO Dario Amodei was asked what a kid finishing high school should do now. His answer: "AI can do entry-level law work. It can do entry-level finance. It can do entry-level consulting We can't deny that it will also eliminate jobs, and probably a large number of them" In a few minutes he names which jobs go first and the one skill that keeps you in the game His own prediction: 50 percent of entry-level white-collar jobs disrupted in the next 1 to 5 years Prompts → Agents → AI Coworkers → Entry-Level Work Done By AI Farming to factories took generations. This time AI hits every step of a career at once His advice fits in one line: "Of course, learn to use AI" The people who start this year won't be competing with AI, they'll be the ones running it Everything below is free. Start today: Anthropic - Prompt Engineering Interactive Tutorial: the fastest way to learn to direct a model Anthropic Courses: from your first prompt to running agents Anthropic Cookbook: real task recipes you can copy today Microsoft - Generative AI for Beginners: 21 lessons, no degree required Microsoft - AI Agents for Beginners: build agents that do the entry-level work for you Hugging Face - Agents Course: free and certified, start to finish Google Cloud - Generative AI: Google's own hands-on notebooks OpenAI Cookbook: code for every common task - Prompt Engineering Guide: the reference everyone links to Andrej Karpathy - Neural Networks Zero to Hero: understand what is under the hood CrewAI: run a small team of AI workers Microsoft - AutoGen: put several agents on one job Awesome LLM Apps: hundreds of real builds to learn from - Short Courses: free 1-hour courses from the people who teach this Total cost: $0 You don't need another year of tuition to learn this. You need to start telling AI what to do 95% will graduate and compete with AI for the same entry-level job The 5% who learn to run it this year will manage the agents doing that job Save this and send it to someone graduating this year

This is not as good as it sounds, because Starlink required many satellites in low Earth orbit to achieve this bandwidth improvement globally. That said, there will ultimately be over 100k SpaceX communications satellites, most of which will be V4 or V5. Good chance that this system carries about half of global traffic.

建议收藏，用到时照着做。做完你能让 DeepSeek 自己决定“要不要调用你写的函数”，并把结果用人话讲回来。 AI 能查天气、查订单，是它自己去查的吗？DeepSeek 官方一句话拆台：模型本身不执行任何函数 这是 DeepSeek 官方 API 文档里 Tool Calls（工具调用）一页。先说结论： 官方原话是，函数要由你自己提供，模型本身不执行具体函数。它只负责说“我想调这个函数、参数是这些”，真正去跑的是你的代码。 先说清词。 工具调用（Tool Calls），是让模型能用外部工具的功能，比如查天气、查数据库。 函数（function），是你自己写好的一段代码，模型只会“点名”它。 tool_call_id（工具调用编号），是模型每次点名时附带的一个号码，你回传结果时要带上它，模型才知道你在回答哪一次。 strict 模式（严格模式，Beta 测试版），是让模型点名时严格按你定义的格式填参数。 一个真实例子：官方示例问“杭州天气怎么样”，模型返回的是 get_weather 这个函数和参数 location 等于杭州，你的代码去查到“24℃”，再交给模型，模型才说“杭州现在 24 度”。 先把话说全： 官方示例用的是非思考模式，请求里加 extra_body 把 thinking 的 type 设成 disabled。 官方写明：从 DeepSeek-V3.2 开始，思考模式下也能用工具调用，细节看官方 Thinking Mode 一页。 官方写明：Chat Completion 接口不支持在对话中间插入不是模型生成的工具调用，要插的话请改用 Anthropic API 或 Responses API。 strict 模式目前是 Beta，要用测试版地址。 一、先看懂整个流程 官方给的执行流程是四步：用户问杭州天气；模型返回要调用 get_weather，参数是杭州；你的代码真的去调用 get_weather，把结果交给模型；模型用自然语言回答“杭州现在 24 度”。 二、照官方示例做一遍 第一步：去 DeepSeek 开放平台拿一个 API key，电脑装好 Python 和 openai 库。 第二步：创建客户端，base_url 写 DeepSeek 官方接口地址（官方文档第一屏就有），api_key 填你的。 第三步：写 tools 清单。每个工具包含 type 等于 function，再写 name（函数名）、description（描述，告诉模型什么时候用）、parameters（参数）。官方示例的函数叫 get_weather，描述是“获取某地天气，用户须先提供地点”，参数 location 是字符串，并且在 required 里。 第四步：发第一次请求，model 官方示例写 deepseek-flash，把 messages 和 tools 一起传，并加上 thinking 关闭的设置。 第五步：从返回里取 tool_calls 的第一项，先把模型这条消息原样追加进 messages。 第六步：你的代码去执行函数，再追加一条 role 是 tool 的消息，带上刚才的 tool_call_id，content 写结果，官方示例写的是 24℃。 第七步：再发一次请求，这次模型就会用自然语言回答。 三、想要更稳：strict 严格模式 第八步：base_url 改成官方文档里写明的 beta 版接口地址（就是原地址后面加 /beta）。 第九步：每个 function 都加上 strict 等于 true。 第十步：服务器会检查你的 JSON Schema（结构说明），格式不合规或用了不支持的类型，会直接返回错误信息。 官方支持的类型是：object、string、number、integer、boolean、array、enum、anyOf，另外 $ref 和 $def 可以复用和做递归。 四、strict 的硬规矩 对象里所有属性都必须写进 required，并且 additionalProperties 必须设成 false。 字符串可以用 pattern（正则）和 format，官方列出的格式有 email、hostname、ipv4、ipv6、uuid，但不支持 minLength 和 maxLength。 数字支持 const、default、minimum、maximum、exclusiveMinimum、exclusiveMaximum、multipleOf。 数组不支持 minItems 和 maxItems。 五、看到什么算成功 第一次返回的是函数点名，不是答案；第二次返回的才是自然语言答案，内容里有你的函数结果。 六、卡住怎么办 strict 报错：先检查是不是漏写 required 或忘了 additionalProperties 设 false，再检查有没有用到 minLength、maxItems 这类官方不支持的写法。 结论：工具调用不是模型替你干活，而是模型“点名”、你的代码“动手”。想让点名更准，就用 strict。把这条复制链接发给他，下次他说 AI 查不到东西，先让他确认函数是不是自己写了。 你卡在第几步？评论区告诉我。同系列前几篇在我主页。 收藏版：5 步速查 一、tools 里写清函数名、描述、参数 二、第一次请求拿到 tool_calls 三、自己执行函数，回传 role 为 tool 的消息并带 tool_call_id 四、第二次请求拿到自然语言答案 五、想更稳就用 beta 地址加 strict 等于 true 常见坑 3 条 一、以为模型会自己执行函数，其实要你的代码去跑。 二、回传结果忘了带 tool_call_id。 三、strict 里漏了 required 或 additionalProperties 设成 false。 复制即用提示词：我想让模型调用一个叫 你的函数名 的函数，它需要这些参数：写参数。请帮我写出符合 DeepSeek Tool Calls 格式的 tools 清单，每个参数都写 description，并改成 strict 模式需要的写法。 S1哥观点：这篇最狠的地方，是官方把话挑明了：模型不执行函数，只负责点名。很多人以为 AI 在后台自己动手，其实动手的永远是你的代码。 真正的信号是：工具调用的难点不在模型，在你把函数描述写得多清楚。就像派活给新人，说明写得含糊，他再聪明也会点错人。 你现在想让 DeepSeek 调用的是什么，查数据还是发消息？评论区告诉我。 今天送：DeepSeek 工具调用速查卡，评论区第一条直接拿。 参考：DeepSeek 官方 API 文档 Tool Calls（S1哥 整理）

not all crypto rewards are created equal. when a platform advertises yield, i think the first thing to check is what holders actually receive and where that value comes from. @RebateLaunch has an interesting approach to this. instead of paying holder rewards in newly issued $rebate, eligible holders receive payouts in the pair asset. on robinhood chain, that's usdg. the platform reports: → $28.5k distributed to holders. → 120,403 individual payouts. → rewards sent several times throughout the day. → no staking or manual claiming required. but the reward distribution is only one part of the model. rebate pad also uses a declining launch tax to address early sniping. → 99% during the initial stage. → 66% in the next stage. → 33% in the following stage. these penalties go into a locked treasury that supports the buyer bonus mechanism. separately, the 0.7% platform fee is used to buy $rebate from the market and burn it. the reported cumulative burn has reached 35.3m tokens, representing 3.53% of the original 1 billion supply. what makes this model worth examining is how several mechanisms connect: ✦ early launch activity contributes to the treasury. ✦ the treasury supports buyer bonuses. ✦ trading fees fund holder payouts and the $rebate buyback-and-burn mechanism. my perspective is that paying rewards in the pair asset is an interesting distinction because holders aren't simply receiving more of the same token. however, attractive payout numbers alone don't prove sustainable yield. trading volume, fee generation, treasury health and long-term user activity still matter. i'd rather evaluate the source of rewards than focus only on the advertised percentage. also, round 3 of the creator campaign closes november 17 at 22:00 utc. the real question is whether rebate pad can maintain this economic cycle as adoption grows. $rebate